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Channel estimation for hybrid Multiple Input Multiple Output (MIMO) systems at Millimeter-Waves/sub-THz is a fundamental, despite challenging, prerequisite for an efficient design of hybrid MIMO precoding/combining. Most works propose sequential search algorithms, e.g., Compressive Sensing (CS), that are most suited to static channels and consequently cannot apply to highly dynamic scenarios such as Vehicle-to-Everything (V2X). To address the latter ones, we leverage recurrent vehicle passages to design a novel Multi Vehicular (MV) hybrid MIMO channel estimation suited for Vehicle-to-Infrastructure (V2I) and Vehicle-to-Network (V2N) systems. Our approach derives the analog precoder/combiner through a MV beam alignment procedure. For the digital precoder/combiner, we adapt the Low-Rank (LR) channel estimation method to learn the position-dependent eigenmodes of the received digital signal (after beamforming), which is used to estimate the compressed channel in the communication phase. Extensive numerical simulations, obtained with ray-tracing channel data and realistic vehicle trajectories, demonstrate the benefits of our solution in terms of both achievable spectral efficiency and mean square error compared to the unconstrained maximum likelihood estimate of the compressed digital channel, making it suitable for both 5G and future 6G systems. Most notably, in some scenarios, we obtain the performance of the optimal fully digital systems.

With the advent of self-driving vehicles, autonomous driving systems will have to rely on a vast number of heterogeneous sensors to perform dynamic perception of the surrounding environment. Synthetic Aperture Radar (SAR) systems increase the resolution of conventional mass-market radars by exploiting the vehicle's ego-motion, requiring a very accurate knowledge of the trajectory, usually not compatible with automotive-grade navigation systems. In this regard, this paper deals with the analysis, estimation and compensation of trajectory estimation errors in automotive SAR systems, proposing a complete residual motion estimation and compensation workflow. We start by defining the geometry of the acquisition and the basic processing steps of Multiple-Input Multiple-Output (MIMO) SAR systems. Then, we analytically derive the effects of typical motion errors in automotive SAR imaging. Based on the derived models, the procedure is detailed, outlining the guidelines for its practical implementation. We show the effectiveness of the proposed technique by means of experimental data gathered by a 77 GHz radar mounted in a forward looking configuration., Comment: 14 pages

Fifth generation (5G) and beyond communication systems open the door to millimeter Wave (mmWave) frequency bands to leverage the extremely large operating bandwidths and deliver unprecedented network capacity. These frequency bands are affected by high propagation losses that severely limit the achievable coverage. The simplest way to address this problem would be to increase the number of installed mmWave base stations (BSs), at the same time augmenting the overall network cost, power consumption and ElectroMagnetic Field (EMF) levels. As alternative direction, here we propose to complement the macro-layer of mmWave BSs with a heterogeneous deployment of Smart Electromagnetic (Smart EM) Entities - namely IAB nodes, Smart Repeaters, Reconfigurable Intelligent Surfaces (RISs) and passive surfaces - that is judiciously planned to minimize the total installation costs while at the same time optimizing the network spectral efficiency. Initial network planning results underline the effectiveness of the proposed approach. The available technologies and the key research directions for achieving this view are thoroughly discussed by accounting for issues ranging from system-level design to the development of new materials.

Buildings and vegetation in the proximity of a terrestrial base station induce a significant additional loss, typically referred to as “clutter loss”, which sums up to free space loss and atmospheric attenuation. Clutter loss is essentially due to the radiowave reflection and diffraction caused by buildings and vegetation, and tends to reduce the interference between terrestrial systems, such as upper 6 GHz (U6G), and satellite systems operating in the same frequency bands. In fact, for example, at low elevation angles, the clutter loss could reach some tens of dB in the U6G band. A novel clutter loss model in urban and suburban environments for frequencies up to 10 GHz is proposed. The model relies on the Monte Carlo simulation approach presented in Report ITU-R P.2402-0, but some limitations have been removed to extend its applicability to more complex scenarios and possibly increase its accuracy for U6G systems. An analytical approach is also proposed to model the clutter loss statistics obtained by properly fitting the obtained statistics for the cities of London and Melbourne. Finally, the proposed model is validated by comparing its results to those obtained by a commercial ray tracer.

Channel knowledge is a fundamental requirement for accurate analog-digital beamforming in hybrid MIMO systems. Conventional channel estimation techniques, e.g., unconstrained maximum likelihood, cannot be used to directly estimate the full MIMO channel in hybrid systems, as the analog beamforming limits the channel observation to the digital side only. We propose a two-stage channel estimation procedure for 6G Vehicle-to-Everything (V2X) systems, leveraging multiple recurrent vehicle passages over the same location, properly clustered in space, in both the analog beam training (stage 1) and the algebraic digital channel estimation (stage 2). We show that the proposed method practically matches the perfect channel knowledge performance on a wide range of Signal-to-Noise Ratio (SNR) values, discussing its behavior with respect to the cluster size.

In the emerging high mobility Vehicle-to-Everything (V2X) communications using millimeter Wave (mmWave) and sub-THz, Multiple-Input Multiple-Output (MIMO) channel estimation is an extremely challenging task. At mmWaves/sub-THz frequencies, MIMO channels exhibit few leading paths in the space-time domain (i.e., directions or arrival/departure and delays). Algebraic Low-rank (LR) channel estimation exploits space-time channel sparsity through the computation of position-dependent MIMO channel eigenmodes leveraging recurrent training vehicle passages in the coverage cell. LR requires vehicles' geographical positions and tens to hundreds of training vehicles' passages for each position, leading to significant complexity and control signalling overhead. Here we design a DL-based LR channel estimation method to infer MIMO channel eigenmodes in V2X urban settings, starting from a single LS channel estimate and without needing vehicle's position information. Numerical results show that the proposed method attains comparable Mean Squared Error (MSE) performance as the position-based LR. Moreover, we show that the proposed model can be trained on a reference scenario and be effectively transferred to urban contexts with different space-time channel features, providing comparable MSE performance without an explicit transfer learning procedure. This result eases the deployment in arbitrary dense urban scenarios.

With the raising interest in automated driving, onboard perception systems are required to perform a wide range of functionalities, from basic emergency braking to mapping of the surroundings. However, current automotive radars are known to suffer from low angular/range resolution and might be unable to meet the high-definition mapping required by high levels of automation. Synthetic Aperture Radar (SAR) systems allow to improve the angular resolution of standard automotive radars, but their imaging performance is constrained by the available bandwidth. In Joint Communication and Sensing (JCS), where the on-board radio is used also to inter-connect road users, cooperation enables SAR performance augmentation thanks to the improved geometric factor, especially for near-isotropic targets (poles, pedestrians, etc.). Furthermore, the combination of communication and sensing allows to trade between bandwidth, cooperation and synthetic aperture so as to optimize the overall JCS performance. In this paper, we propose a cooperative SAR (C-SAR) system for automotive scenarios, where single ego-vehicle SAR images are exchanged with neighboring vehicles thus combined to enhance SAR performance. Preliminary simulations support the proposed idea, enabling SAR imaging improvements in low bandwidth scenarios.

Automated driving requires a huge number of on-board sensors to provide advanced functionalities, from parking assistance to emergency braking and environment mapping for target recognition/classification. While low-cost automotive-legacy radars are mostly used for target detection due to their limited angular resolution, vehicular Synthetic Aperture Radar (SAR) is emerging as a promising imaging solution, provided that the motion is known with high accuracy. This paper assesses the benefits of a navigation-augmented SAR system exploiting multiple on-board sensors, e.g., Global Navigation Satellite System (GNSS), Inertial Measurement Units (IMUs), odometers and steering angle sensors. The results confirm the potential of the proposed multi-sensor-aided SAR system to obtain centimeter-level accurate images of the driving scenario.

This paper deals with the analysis, estimation, and compensation of trajectory errors in automotive-based Synthetic Aperture Radar (SAR) systems. First of all, we define the geometry of the acquisition and the model of the received signal. We then proceed by analytically evaluating the effect of an error in the vehicle's trajectory. Based on the derived model, we introduce a motion compensation (MoCo) procedure capable of estimating and compensating constant velocity motion errors leading to a well-focused and well-localized SAR image. The procedure is validated using real data gathered by a 77 GHz automotive SAR with MIMO capabilities., Comment: 6 pages

Automotive synthetic aperture radar (SAR) systems are rapidly emerging as a candidate technological solution to enable a high-resolution environment mapping for autonomous driving. Compared to lidars and cameras, automotive-legacy radars can work in any weather condition and without an external source of illumination, but are limited in either range or angular resolution. SARs offer a relevant increase in angular resolution, provided that the ego-motion of the radar platform is known along the synthetic aperture. In this paper, we present the results of an experimental campaign aimed at assessing the potential of a multi-beam SAR imaging in an urban scenario, composed of various targets (buildings, cars, pedestrian, etc.), employing a 77 GHz multiple-input multiple-output (MIMO) radar platform based on a mass-market available automotive-grade technology. The results highlight a centimeter-level accuracy of the SAR images in realistic driving conditions, showing the possibility to use a multi-angle focusing approach to detect and discriminate between different targets based on their angular scattering response., Comment: 6 pages

The beam sweeping procedure estimates the beamforming directions, or beams, to be used for downlink millimeter-wave cellular transmissions to an incoming user entering the cell. We propose three new approaches for the choice of the sequence by which beams are explored (the beam-search sequence), in order to reduce the average time to find the directions. The proposed methods exploit a) the correlation among angles of departure of the incoming user and its nearest, already connected user, and b) the position of both the incoming and its nearest user. For point a), the nearest-neighbor-beam search method starts from the beam of the nearest user, and then explores other beams, with directions progressively farther away (in the angular domain) from the first beam. In the beam-and-angle search, the beam-search sequence is optimized by exploiting the knowledge of both users' position and the nearest-user beam. We also propose the simplified beam-and-angle search, which still exploits the nearest-user information, although in a suboptimal and simpler fashion. Based on the channel model of the 3rd generation partnership project, we analytically design the optimal beam-and-angle search sequence. We assess the performance of the proposed solutions, in terms of their average discovery time, also in comparison with existing approaches.

International audience; This work deals with the synthesis of time-modulated arrays (TMAs) for cognitive radio (CR) systems, in which the harmonic radiations arising from the periodic modulation of the elements in the time-domain are exploited as independent channels for multi-point-to-point communications. The descriptors of advanced time-modulating sequences using sum-of-weighted cosine (SWC) waveforms are optimized through the particle swarm optimization (PSO) with the aim of maximizing the overall signal-to-interference-plus-noise ratio (SINR) on receive and of increasing the antenna directivity. Representative numerical results are presented and discussed in comparison with the ones obtained from classical TMA implementations based on rectangular pulse sequences in order to highlight the achievable performance improvements.

In this paper, we investigate a wireless communication scenario, where polarization multiplexing is exploited to increase the spectral efficiency. Independent modems over each polarization are considered, with communication links affected by phase noise and cross-polarization interference (XPI). We devise a novel per-polarization soft decision-directed iterative receiver with separate a posteriori probability-based synchronization and decoding. The synchronization algorithm relies on a minimum mean square error-based master–slave phase estimation followed by the cancellation of the XPI on the polarization of interest and requires no statistical knowledge of the phase noise process. The performance of the proposed iterative receiver is investigated for a pilot symbol-assisted low-density parity-check-coded quadrature amplitude modulation scheme.

Large antenna arrays have been always exploited for radar applications in order to provide very high spatial resolution, beam-forming and null-steering. These capabilities have been found to be extremely appealing for communication systems too because of their potential to provide highly-directive beams as the best solution to reduce interference and increase the overall capacity, allowing spectral reuse through spatial diversity. Nowadays the big challenge is not only related to the maximization of the overall system performance - which have been proven to be very effective when considering full-phased/full-digital architectures - but is mainly focused on the economic side: the real goal is to keep cost and energy consumption sufficiently low to obtain affordable products. This work focuses on millimeter-wave wireless systems (nun-Waves), and presents an overview of the application scenarios for 5G (and beyond) networks, together with the main related challenges and finally explores some techniques that can be adopted to design energy- and cost-efficient phased array antennas.

This study discusses synchronisation in phase noise-impaired spectrally efficient communication systems employing high-order modulations. In particular, an iterative receiver, where demodulation and decoding are separate from maximum a posteriori probability (MAP) synchronisation, is presented. The authors’ separate approach is tailored to the design of pragmatic iterative receiver schemes employing ‘off-the-shelf’ demodulation and decoding blocks. This allows full compatibility with already existing systems, which is attractive from the implementation viewpoint. The proposed MAP synchronisation algorithm also requires very limited knowledge of the phase noise process and achieves near coherent performance with moderate computational complexity. Although the approach is very general, the authors discuss its performance for low-density parity-check-coded pilot symbol-aided quadrature amplitude modulation schemes, demonstrating that a significantly lower computational complexity can be achieved with respect to benchmark joint receivers.

Current trends for mmWave (millimeter Wave) MIMO communications exploit large antenna arrays with hundreds of radiating elements, which provide highly-directive beams allowing spectral reuse through spatial diversity. Unconventional arrays are proposed for the design of base station systems for 5G applications, with the goal to simplify the required beamforming architecture with respect to standard fully-populated arrangements. This article presents an overview of some design activities, covering two different unconventional array solutions, showing implementation challenges as well as the latest achievements on this field.

Large antenna arrays and millimeter-wave (mmWave) frequencies have been attracting growing attention as possible candidates to meet the high requirements of future 5G mobile networks. In view of the large path loss attenuation in these bands, beamforming techniques that create a beam in the direction of the user equipment are essential to perform the transmission. For this purpose, in this paper, we aim at characterizing realistic antenna radiation patterns, motivated by the need to properly capture mmWave propagation behaviors and understand the achievable performance in 5G cellular scenarios. In particular, we highlight how the performance changes with the radiation pattern used. Consequently, we conclude that it is crucial to use an accurate and realistic radiation model for proper performance assessment and system dimensioning., Comment: to be published in 2018 IEEE ICC Communications QoS, Reliability, and Modeling Symposium (ICC18 CQRM), Kansas City, USA, May 2018

In this paper, an innovative Minimum Mean Square Error (MMSE)-based iterative synchronization algorithm is devised for a dually-polarized wireless link, affected by phase noise and Cross-Polarization Interference (XPI), with independent transmission streams over the two polarizations. A novel perpolarization soft decision-directed iterative receiver with separate A Posteriori Probability (APP)-based synchronization and decoding is proposed. The key idea of the proposed synchronization algorithm relies on an MMSE-based (master-slave) phase estimation followed by cancellation of the XPI on the polarization of interest. An attractive feature of the proposed approach is that it requires no statistical knowledge of the phase noise process. The performance of the proposed iterative receiver is investigated with a pilot symbol-assisted Low-Density Parity-Check (LDPC)-coded Quadrature Amplitude Modulation (QAM) scheme.

This paper focuses on phase noise-impaired communications. An efficient Maximum A-posteriori Probability (MAP) iterative synchronization algorithm, where detection and decoding are performed separately from phase estimation, is proposed. This approach has the following key advantages: (i) its computational complexity is relatively low and its performance is near optimal; (ii) it requires very limited statistical knowledge of the phase noise process; and (iii) it enables the direct use of “off-the-shelf” demodulation and decoding blocks. These features are particularly attractive from the implementation viewpoint, as they lead to the design of effective pragmatic high-order coded modulated schemes. The proposed iterative synchronization and decoding algorithm, evaluated for Low-Density Parity-Check (LDPC)-coded pilot symbol-assisted Quadrature Amplitude Modulation (QAM) schemes, entails a negligible energy efficiency loss with respect to optimized joint decoding and phase estimation approaches, with significantly lower computational complexity.

We investigate the problem of ranging estimation in a dense multipath environment, applying a technique based on the power delay profile analysis of the received UWB signal. First, we present the theory of segmentation for nonstationary processes, exploited for delay estimation, then we validate the estimator considering real UWB indoor signals, thus showing that good accuracy can be reached even in dense non-LoS multipath. Finally, ranging in a realistic UWB system with a low number of interfering nodes has been simulated.